- 🎓 B.E. in Artificial Intelligence & Machine Learning
- 🏫 BIET Davangere
- 📊 CGPA: 8.1 / 10
- 🔬 Passionate about Machine Learning, Deep Learning and MLOps
- 🌱 Currently Learning Kubernetes, AWS and LLM Fine-Tuning
- 🏆 Best Project Award 2026
- 🥈 2nd Place Mini Project Exhibition
- 💼 Looking for AI Engineer / ML Engineer Opportunities
Languages & Core
Deep Learning & ML
MLOps & Deployment
Databases & Visualization
Machine Learning
Deep Learning
Data Science
Data Analytics
Computer Vision
Natural Language Processing
Large Language Models
MLOps
Model Deployment
Prompt Engineering
Production-grade MLOps system — Built for Adobe ML Engineer Application
| Feature | Details |
|---|---|
| 🎯 Model | EfficientNet-B0 — 86.85% accuracy |
| 📦 Dataset | 14,000 real images (Intel Image Classification) |
| ⚖️ A/B Testing | 80% stable / 20% canary routing |
| 📊 MLflow | Model registry, versioning, experiment tracking |
| 🔍 Drift Detection | Evidently AI — auto retraining triggers |
| 🐳 Deployment | Docker + Railway (publicly live) |
| ✅ CI/CD | GitHub Actions — passing |
PyTorch FastAPI MLflow Docker EfficientNet Evidently AI GitHub Actions Railway
🏆 Best Project Award 2026 — Nirmana Exhibition, BIET Davangere
| Feature | Details |
|---|---|
| 🧠 Blood Biomarker ANN | 91.26% accuracy |
| 🦴 Swin Transformer (X-rays) | 85.83% accuracy / 94.95% recall |
| 🤖 AI Assistant | Groq-powered natural language queries |
| 📊 Validation | 5-fold cross-validation (±1.78% variance) |
PyTorch Swin Transformer Streamlit Groq API SQLite
Data Science Internship — Qspiders, Bengaluru
| Metric | Value |
|---|---|
| Accuracy | 92% |
| AUC-ROC | 0.89 |
| Recall | 88% |
| Prediction Time | weeks → < 2 seconds |
XGBoost SHAP Scikit-learn Streamlit SMOTE
🥈 2nd Place — Mini Project Exhibition, JIT College 2025
- IBM Granite-3.1 LLM + Google TTS → MP3 audiobooks
- Flask backend integrating 2 production AI APIs seamlessly
Flask IBM Granite-3.1 Google TTS LLM Orchestration
🔹 Building Production ML Systems
🔹 Learning Kubernetes & AWS
🔹 Exploring LLM Fine-Tuning
🔹 Developing MLOps Pipelines
🔹 Open to AI/ML Engineer Opportunities